Nature presents examples of active sensing which are unique, sophisticated and incredibly fascinating. There are animals that sense the environment actively, for example through echolocation, which have evolved their capabilities over millions of years and that, as a result of evolution, have developed unique in-built sensing mechanisms that are often the envy of synthetic systems. This book presents some of the recent work that has been carried out to investigate how sophisticated sensing techniques used in nature can be applied to radar and sonar systems to improve their performance. Topics covered include biosonar inspired signal processing and acoustic imaging from echolocating bats; enhanced range resolution: comparison with the matched filter; air-coupled sonar systems inspired by bat echolocation; analysis of acoustic echoes from bat-pollinated plants; the biosonar arms race between bats and insects; biologically inspired coordination of guidance and adaptive radiated waveform for interception and rendezvous problems; cognitive sensor/ processor system framework for target tracking; the biosonar of the Mediterranean bottlenose dolphins; human echolocation; and polarization tensors and object recognition in weakly electric fish. Biologically-Inspired Radar and Sonar is essential reading for radar and sonar practitioners in academia and research, governmental and industrial organisations, engineers working in signal processing and sensing, and those with an underlying interest in the interaction between natural sciences and engineering.

Radar and sonar systems play a key role in many modern defence and civilian sensing applications. They are used to accomplish a large variety of tasks which include the detection and classification of targets, the acquisition of intelligence information, imaging, autonomous navigation and collision avoidance.

No biological system more immediately evokes thinking about technology than the sonar sense of bats [1,2]. This chapter describes a novel approach to sonar signal processing that has been identified from experiments carried out with echolocating bats. Bats use their biosonar to perceive target distance (range), target azimuth (crossrange), and target shape using wideband signals, and target velocity and fluttering motion using narrowband signals [2-4]. Being able to perceive these components in real-time while flying through surroundings that vary from simple to complex makes them a very valuable source of engineering inspiration. From a technological perspective, the parallels between biosonar and pulse-echo sensing by man-made radar and sonar systems are very compelling [5]. Moreover, progress towards understanding biosonar has benefitted from applying radar and sonar concepts to biology [5-8]. Furthermore, the historical development of radar and sonar technology overlapped with the discovery and advancement of research on biosonar [1,2,9,10]. Broadcast transmission, echo reception, signal-processing, display of images, and guidance of motion are recognizable functions both in biosonar [2,5] and man-made systems [9,10]. These functions correspond fairly well to specific stages in a block diagram of an engineered system. In biosonar, however, these functions are spread across several stages of biological and perceptual processing because they are interrelated and are carried out in a distributed fashion by overlapping biological structures [2]. The content of this chapter follows a signal-processing theme across stages in the bat's auditory system that comprises its sonar imaging system. At several stages in this story, operations seem similar to practices used in some man-made systems, only to have their outputs treated quite differently at higher stages.

In this chapter, we have presented a baseband receiver that allows an analytical treatment of the output of a SCAT-like processing and that can also be applied to RF signals. The output of the Baseband SCAT (BSCT) has been compared analytically with that of a conventional matched filter for two closely spaced scatterers. Results have shown that a bat-inspired spectrogram transformation can provide better range resolution performance than that of a conventional matched filter. The results have been derived analytically and then verified with a set of laboratory experiments at ultrasound, using phantom echoes and real targets, to assess the robustness of the algorithm with respect to operational hardware limitations and propagation effects.

This chapter is a review of work on biomimetic sonar systems developed in the last 15 years which are inspired by the bat echolocation process. Inspiration from bat biosonar is not intended as a bare transfer of features from the bat biosonar into an engineering sonar system. The various contributions considered in this chapter aim at developing one specific technology or localisation method using cues from the bat biosonar. In some cases, for example, the reproduction of one or more functionalities of the bat echolocation process is targeted to understand the functionalities themselves by implementing them into mathematical formulations or attempting the same tasks through empirical investigation.

In this paper, we analyse a real set of data containing high range resolution profiles (HRRP) of unpollinated corollas of Cobaea scandens and of an inflorescence of Rhytidophyllum auriculatum, which are plants typically pollinated by bats. These were collected by transmitting a synthetic wideband linear chirp with an acoustic radar capable of a very high range resolution. Two C. scandens flowers taken at different stage of maturity were measured to investigate if and how the information contained in the flowers' echo acoustic signatures changes as the flower wilts and hence stops producing nectar and loses attractiveness. These were then modified, by manually removing the petals, in order to study feature-specific contributions to the scattering. The acoustic signatures of the C. scandens and the R. auriculatum do not change as a result of pollination by a bat and indeed the same individual flower can be pollinated multiple times as long as it contains nectar. The analysis presented in this chapter is subtly different from investigating pre and post bat fed plants but it demonstrates the acoustic changes as the flower degrades, and hence it is a valid experiment for this bat-pollinated plant species. The signatures are analysed and results are discussed with respect to the task of classification of manmade targets.

The evolutionary arms-race between bats and insects is a classic example of coevolution between predator and prey. The counter adaptations of night flying insects to bat echolocation calls and subsequent adaptations of bats to combat the hearing abilities of many insects makes it one of the few examples of species responding to selective pressure from one another. A true arms-race of the sensory systems and behaviour across species. There are already examples of some of the strategies that bats use to try and counter moth adaptations found within the technological world. The stealth echolocation utilised by B. barbastellus is analogous to low-probability-of-intercept (LPI) radar, whilst the use of bat-inspired harmonics in waveform design has been shown to potentially bring advantages in enhancing radar performance in unfavourable conditions. Although a great deal has been learnt about the ongoing acoustic battle between bats and insects, there is of course still much to uncover about this fascinating interaction. Much of the work done thus far has focused on moths, yet numerous night flying insects are preyed upon by bats, most of which have yet to be studied in similar detail. More advanced techniques have facilitated the study of bat-insect interactions in the field, with 3D microphone arrays and 3D infrared videography revealing even more about how the real-world encounters between species are played out. There are likely to be many more secrets to this particular war that could advance the development of radar and sonar, and we are only just beginning to unearth them.

In this chapter, we take inspiration from the bat and develop an algorithm that guides an airborne radar interceptor towards a target by jointly developing an optimal guidance and automatically adapting and optimising the transmitted waveform on a pulse-to-pulse basis. The algorithm uses a Kalman filter to predict the relative position and speed of the interceptor with respect to the target.

In this chapter we examine the emerging topic of artificial cognition and its application to target tracking. Most sensor/processor systems employ a feed-forward processing chain in which they first perform some low-level processing of received sensor data (such as obtaining target detections) and then pass the processed data on to some higher-level processor (such as a tracker or classifier), which extracts information (such as target kinematic parameters and target type) to achieve a system objective. Tracking and classification can be improved using adaptation between the information extracted from the sensor/processor and the design and transmission of subsequent illuminating waveforms. As such the application of artificial cognition to sensor/processor systems offers much promise for improved sensing and processing [1-7].

In this chapter, we described the analysis carried out on the sonar clicks emitted by Mediterranean Bottlenose dolphins in both audio and ultrasonic bands. We found that most of the sonar clicks emitted when the dolphin is in front of the hydrophone can be modelled by exponential or by Gaussian broadband multicomponent signals. The parameters of these two models have been estimated. The components characterizing each pulse are generally the first or the first two most powerful ones and the fitting with the data seems to be very good in both audio and ultrasonic band. Actually, the meaning of the sonar clicks in the audio band signals is not clear. Maybe, as reported by Zimmer [20], they can be “machinery noise”, that is noise produced by dolphins in emitting the ultrasonic pulses used for the echolocation. In ultrasonic band the most powerful frequency component is located around 24 kHz, almost four octaves under the frequency peak measured for the Oceanic Bottlenose dolphins. This phenomenon can be mainly due to the differences in the Oceanic and Mediterranean environments. However, the efficiency of the dolphin sonar is not only due to the broadband characteristic of the sonar click signals, then to the very high range resolution. Dolphins are able to use as well a multi-perspective, multi-waveforms approach to sense the targets, moving around their preys and changing their trajectories [21], the power and the Pulse Repetition Frequency (PRF) based upon what they have learned from the previous sonar clicks [11,22]. Finally, they use their trained brain to control the whole biosonar process allowing for versatility and continuous learning [4]. It is, of course, not trivial to build a technological sonar similar to that of the marine mammals but the idea of cognitive radars (and so sonars) has been already proposed in 2006 by Haykin in [23] and some progress has been made since then along that path.

This paper will focus on the echolocation abilities of humans, describing the acoustic properties of their emissions as well as the acuity with which they are able to discriminate certain object properties - distance, angle (horizontal and vertical), size, shape and material - and the various cues that they might use to do this.

In this paper, polarization tensors provide a plausible low-dimensional representation of the response of an isolated object in an electric field that captures shape information about the object independently of position and transform naturally under rotation. It would certainly be a candidate for the basis of human-made algorithms to replicate some of the capabilities of electrosensing fish. Polarization tensors can also be used for locating and characterizing metal objects using eddy current inductive measurements, and similar theory has been developed for far-field radio and sound wave remote sensing methods.